Benchopt is a package to simplify and make more transparent and reproducible the comparisons of optimization algorithms. The Ridge consists in solving the following program:

$$\min_w\frac{1}{2} \Vert y - Xw \Vert_2^2 + \frac{\lambda}{2} \Vert w \Vert_2^2$$

where $n$ (or n_samples) stands for the number of samples, $p$ (or n_features) stands for the number of features and

$$y \in \mathbb{R}^n, \ X \in \mathbb{R}^{n \times p}$$

This benchmark can be run using the following commands:

$ pip install -U benchopt
$ git clone https://github.com/benchopt/benchmark_ridge
$ cd benchmark_ridge/

To demonstrate the use of benchopt, one can run, from the benchmark_lasso folder:

$ benchopt install . -s sklearn -s python-pgd --env
$ benchopt run . --config example_config.yml --env

Alternatively, one can use the command line interface to select which problems, datasets and solvers are used:

$ benchopt run ./benchmark_ridge -s sklearn -d leukemia --max-runs 10 --n-repetitions 10

Use benchopt run -h for more details about these options, or visit https://benchopt.github.io/api.html.

If you run into some errors when running the examples present in this Readme, try installing the development version of benchopt:

$ pip install -U git+https://github.com/benchopt/benchopt

If issues persist, you can also try running the benchmark in local mode with the -l option, e.g.:

$ benchopt run . -l -s sklearn -d leukemia --max-runs 10 --n-repetitions 10

Note that in this case, only solvers whose dependencies are installed in the current env will be run.